Robotics
♻ ★ Cross-Category Functional Grasp Transfer
Generating grasps for a dexterous hand often requires numerous grasping
annotations. However, annotating high DoF dexterous hand poses is quite
challenging. Especially for functional grasps, requiring the hand to grasp the
object in a specific pose to facilitate subsequent manipulations. This prompts
us to explore how people achieve manipulations on new objects based on past
grasp experiences. We find that when grasping new items, people are adept at
discovering and leveraging various similarities between objects, including
shape, layout, and grasp type. Considering this, we analyze and collect
grasp-related similarity relationships among 51 common tool-like object
categories and annotate semantic grasp representation for 1768 objects. These
objects are connected through similarities to form a knowledge graph, which
helps infer our proposed cross-category functional grasp synthesis. Through
extensive experiments, we demonstrate that the grasp-related knowledge indeed
contributed to achieving functional grasp transfer across unknown or entirely
new categories of objects.
♻ ★ Guiding Reinforcement Learning with Incomplete System Dynamics IROS 2024
Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni, Lixian Zhang
Model-free reinforcement learning (RL) is inherently a reactive method,
operating under the assumption that it starts with no prior knowledge of the
system and entirely depends on trial-and-error for learning. This approach
faces several challenges, such as poor sample efficiency, generalization, and
the need for well-designed reward functions to guide learning effectively. On
the other hand, controllers based on complete system dynamics do not require
data. This paper addresses the intermediate situation where there is not enough
model information for complete controller design, but there is enough to
suggest that a model-free approach is not the best approach either. By
carefully decoupling known and unknown information about the system dynamics,
we obtain an embedded controller guided by our partial model and thus improve
the learning efficiency of an RL-enhanced approach. A modular design allows us
to deploy mainstream RL algorithms to refine the policy. Simulation results
show that our method significantly improves sample efficiency compared with
standard RL methods on continuous control tasks, and also offers enhanced
performance over traditional control approaches. Experiments on a real ground
vehicle also validate the performance of our method, including generalization
and robustness.
comment: Accepted to IROS 2024